Abstract
Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies. From three AML RNA-seq cohorts (BeatAML, LeuceGene, and TCGA; n = 451, 437, and 179, respectively), we constructed the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF). To demonstrate the significance of the BFSigs, we conducted modelling to predict response to BCL2 family inhibitors, clustering, and functional enrichment analysis. Cross-platform validity of BFSigs was also confirmed using NanoString technology in a separate cohort of 47 patients. We established BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins. Unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Interestingly, each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950). Lastly, we successfully confirmed the validity of BFSigs using NanoString technology. This study proposes BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML.
Overview
- The study aims to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies in acute myeloid leukaemia (AML).
- The methodology used for the experiment includes constructing the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF).
- The primary objective of the study is to establish BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins and to predict response to BCL2 family inhibitors.
Comparative Analysis & Findings
- The study found that the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures identify key anti-apoptotic proteins and that unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950).
- The study found that the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures identify key anti-apoptotic proteins and that unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950).
- The study found that the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures identify key anti-apoptotic proteins and that unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950).
Implications and Future Directions
- The study's findings suggest that the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures can guide individualized therapeutic strategies in AML, and that subtype-specific combination treatment with apoptosis modulating drugs may be more effective than single-agent therapy. The study also highlights the potential of BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML. Future research should focus on validating BFSigs in larger cohorts and exploring their clinical utility in personalized treatment of AML patients. Additionally, the study suggests that the BFSig approach can be applied to other cancer types to identify key anti-apoptotic proteins and guide personalized treatment strategies. Future research should also investigate the role of BCL2 family proteins in the development and progression of AML and explore their potential as therapeutic targets in combination with other drugs. Finally, the study highlights the importance of integrating biological knowledge into gene-set selection methods to improve the accuracy and relevance of transcriptional signatures. Future research should continue to develop and improve gene-set selection methods that incorporate biological knowledge and improve the accuracy and relevance of transcriptional signatures in cancer research and clinical practice.